In what is a federally funded phase 2 programme, Quantum Ventura is creating state-of-the art cybersecurity applications for the US Department of Energy under the Small Business Innovation Research (SBIR) Program. The programme is focused on “Cyber threat-detection using neuromorphic computing,” which aims to develop an advanced approach to detect and prevent cyberattacks on computer networks and critical infrastructure using brain-inspired artificial intelligence.
“Neuromorphic computing is an ideal technology for threat detection because of its small size and power, accuracy, and in particular, its ability to learn and adapt, since attackers are constantly changing their tactics,” said Srini Vasan, President and CEO of Quantum Ventura. “We believe that our solution incorporating BrainChip’s Akida will be a breakthrough for defending against cyber threats and address additional applications as well.”
“This project with the Department of Energy offers an ideal opportunity to demonstrate how Akida opens up new possibilities in cybersecurity, including the ability to run complex AI algorithms at the edge, reducing the dependency on the cloud” explained Rob Telson, Vice President of Ecosystems & Partnerships at BrainChip.
The Akida neural processor and AI IP can find unknown, repeating patterns in vast amounts of noisy data, which is an asset in cyber threat detection. Once Akida learns what normal network traffic patterns look like, it can detect malware, attack signatures, and other types of malicious activity. Because of Akida’s ability to learn on device in a secure fashion, without need for cloud retraining, it can quickly learn new attack patterns, enabling it to easily adapt to emerging threats.
BrainChip IP supports incremental learning, on-chip learning, and high-speed inference within micro watt to milli-watt power budgets, making it suitable for advanced AI/ML devices such as intelligent sensors, medical devices, high-end video-object detection, and ADAS/autonomous systems.
Akida is an event-based technology that has been found to be lower power than conventional neural network accelerators, providing energy efficiency with high performance delivering AI solutions previously not possible on even battery-operated or fan-less embedded, edge devices.
